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Development and Performance Validation of a Navigation System for an Underwater Vehicle

Published online by Cambridge University Press:  26 January 2016

R. Ramesh ,
V. Bala Naga Jyothi ,
N. Vedachalam ,
G.A. Ramadass  and
M.A. Atmanand
R. Ramesh*
Affiliation:
(National Institute of Ocean Technology, Ministry of Earth Sciences, Chennai, India)
V. Bala Naga Jyothi
Affiliation:
(National Institute of Ocean Technology, Ministry of Earth Sciences, Chennai, India)
N. Vedachalam
Affiliation:
(National Institute of Ocean Technology, Ministry of Earth Sciences, Chennai, India)
G.A. Ramadass
Affiliation:
(National Institute of Ocean Technology, Ministry of Earth Sciences, Chennai, India)
M.A. Atmanand
Affiliation:
(National Institute of Ocean Technology, Ministry of Earth Sciences, Chennai, India)
*
(E-mail: [email protected])
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Abstract

Underwater position data is a key requirement for the navigation and control of unmanned underwater vehicles. The proposed navigation scheme can be used in any vessel or boat for any shallow water vehicle. This paper presents the position estimation algorithm developed for shallow water Remotely Operated Vehicles (ROVs) using attitude data and Doppler Velocity Log data with the initial position from the Global Positioning System (GPS). The navigational sensors are identified using the in-house developed simulation tool in MATLAB, based on the requirement of a position accuracy of less than 5%. The navigation system is built using the identified sensors, Kalman filter and navigation algorithm, developed in LabVIEW software. The developed system is tested and validated for position estimation, with an emulator consisting of a GPS-aided fibre optic gyro-based inertial navigation system as a reference, and it is found that the developed navigation system has a position error of less than 5%.

Keywords

EmulatorDoppler Velocity LogPosition estimationRemotely Operated Vehicle
Type
Research Article
Information
The Journal of Navigation , Volume 69 , Issue 5 , September 2016 , pp. 1097 - 1113
Copyright
Copyright © The Royal Institute of Navigation 2016 

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References

REFERENCES

Acquaris. (2012). Acquaris Model, Astech Thales GPS receiver. http://ashgps.com/mirror/20130710/Marine%20Survey/Aquarius-Sagitta-3011/Manuals/Sagitta%20Seismic.pdf. Accessed 15 December 2012.Google Scholar
Allan, D. W. (1966). Statistics of atomic frequency standards. Proceedings of the IEEE, 54(2), 221–230.CrossRefGoogle Scholar
Anonsen, K. B., and Hallingstad, O. (2007). Sigma point Kalman filter for underwater terrain-based navigation. Proceedings of the IFAC Conference on Control Applications in Marine Systems, Bol, Croatia. (pp. 106–110).CrossRefGoogle Scholar
Braga, J., Healey, A. J., and Sousa, J. (2012). Navigation scheme for the LSTS SEACON vehicles: Theory and application. In Navigation. Guidance and Control of Underwater Vehicles, 3(1), 69–75).Google Scholar
Christensen, R., Fogh, N., la Cour-Harbo, A., and Bisgaard, M. (2008). Inertial navigation system. Department of Control Engineering in Aalborg University.Google Scholar
Cruz, N., Matos, A., de Sousa, J. B., Pereira, F. L., Silva, J., Silva, E., and Dias, E. B. (2003). Operations with multiple autonomous underwater vehicles: the PISCIS project. In Second Annual Symposium on Autonomous Intelligent Networks and Systems AINS.Google Scholar
de Morais, F. B. (2013). An Advanced Navigation System for Remotely Operated Vehicles. Department of Engineering Cybernatics, Norwegian University of Science and Technology (NTNU).Google Scholar
El-Sheimy, N., Hou, H. and Niu, X. (2008). Analysis and modeling of inertial sensors using Allan variance. Instrumentation and Measurement, IEEE Transactions on, 57(1), 140–149.CrossRefGoogle Scholar
Ellingsen, H. (2008). Development of a Low-Cost Integrated Navigation System for USVs. Department of Engineering Cybernetics, Norwegian University of Science and Technology, Trondheim, Norway.Google Scholar
Fallon, M. F., Papadopoulos, G., Leonard, J. J. and Patrikalakis, N. M. (2010). Cooperative AUV navigation using a single maneuvering surface craft. The International Journal of Robotics Research, 29(12), 1461–1474.CrossRefGoogle Scholar
Foss, H. T. and Meland, E. T. (2007). Sensor Integration for Nonlinear Navigation System in Underwater Vehicles. Department of Engineering and Cybernetics, Norwegian University of Science and Technology (NTNU).Google Scholar
Fossen, T.I. (1994). Guidance and Control of Ocean Vehicles, John Wiley.Google Scholar
Fossen, T.I. (2012). Low-cost integrated navigation systems for autonomos underwater vehicles. Plenary paper presented at the XXXIII Jornadas de Automatica, Vigo, Spain.Google Scholar
Geng, Y., Martins, R., and Sousa, J. (2010). Accuracy analysis of DVL/IMU/magnetometer integrated navigation system using different IMUs in AUV. In Control and Automation (ICCA), 2010 8th IEEE International Conference on (pp. 516–521). IEEE.Google Scholar
Grewal, M. S., Weill, L. R. and Andrews, A. P. (2001). Global positioning systems, inertial navigation, and integration. John Wiley & Sons.Google Scholar
Healey, A. J., An, E. P. and Marco, D. B. (1998). Online compensation of heading sensor bias for low cost AUVs. In Autonomous Underwater Vehicles, 1998. AUV'98. Proceedings Of The 1998 Workshop on Underwater Navigation (pp. 35–42). IEEE.Google Scholar
Hegrenaes, O., Berglund, E. and Hallingstad, O. (2008). Model-aided inertial navigation for underwater vehicles. Robotics and Automation, ICRA 2008. IEEE International Conference on (pp. 1069–1076), Pasadena, CA, USA.CrossRefGoogle Scholar
Hofmann-Wellenhof, B., Legat, K. and Wieser, M. (2011). Navigation: principles of positioning and guidance. Springer Science & Business Media.Google Scholar
Honeywell magnetic sensors. (2014). http://www51.honeywell.com/aero/common/documents/myaerospacecatalog-documents/Missiles-Munitions/HMC6343.pdf. Accessed 20 January 2014.Google Scholar
Hou, H. (2004). Modelling inertial sensors errors using Allan variance. University of Calgary, Department of Geomatics Engineering, University of Calgary, Alberta.Google Scholar
Jalving, B., Gade, K., Hagen, O. K. and Vestgard, K. (2003). A toolbox of aiding techniques for the HUGIN AUV integrated inertial navigation system. In OCEANS 2003. Proceedings (Vol. 2, pp. 1146–1153). IEEE.Google Scholar
Jalving, B., Gade, K., Svartveit, K., Willumsen, A. and Sorhagen, R. (2004). DVL velocity aiding in the HUGIN 1000 integrated inertial navigation system. Modelling Identification and Control, 25(4), 223–236. Nice, France.CrossRefGoogle Scholar
Lammas, A. K., Sammut, K. and He, F. (2007). A 6 DoF navigation algorithm for autonomous underwater vehicles. In OCEANS 2007-Europe (pp. 1–6). IEEE.Google Scholar
Leader, D.E. (1994). Kalman Filter Estimation of Underwater Vehicle Position and Attitude Using a Doppler Velocity Aided Inertial Motion Unit, Engineer Degree Thesis, Massachusetts Institute of Technology and Woods Hole Oceanographic Institution, Massachusetts, September 1994.CrossRefGoogle Scholar
Li, W. and Wang, J. (2013). Effective adaptive Kalman filter for MEMS-IMU/magnetometers integrated attitude and heading reference systems. Journal of Navigation, 66(1), 99–113.CrossRefGoogle Scholar
Li, W., Wu, W., Wang, J. and Lu, L. (2013). A fast SINS initial alignment scheme for underwater vehicle applications. Journal of Navigation, 66(2), 181–198.CrossRefGoogle Scholar
Liansheng, L. and Tao, J. (2011). Research on Strap-down Inertial Navigation System Simulation. Intelligent Computation Technology and Automation (ICICTA), 2011 International Conference on (Vol. 2, pp. 1168–1171). IEEE.CrossRefGoogle Scholar
LinkQuest Incl. (2013). http://www.link-quest.com/html/NavQuest600M.pdf. Accessed on 5th January 2013.Google Scholar
Mandt, M., Gade, K. and Jalving, B. (2001). Integrating DGPS-USBL position measurements with inertial navigation in the HUGIN 3000 AUV. Proceedings of the 8th Saint Petersburg International Conference on Integrated Navigation Systems, Saint Petersburg, Russia.Google Scholar
Marco, D. B. and Healey, A. J. (2001). Command, control, and navigation experimental results with the NPS ARIES AUV. IEEE Journal of Oceanic Engineering, 26(4), 466–476.CrossRefGoogle Scholar
Narayanaswamy, V., Raju, R., Durairaj, M., Ananthapadmanabhan, A., Annamalai, S., Ramadass, G. A. and Atmanand, M. A. (2013). Reliability-Centered Development of Deep Water ROV ROSUB 6000. Marine Technology Society Journal, 47(3), 55–71.CrossRefGoogle Scholar
Panish, R. and Taylor, M. (2011). Achieving high navigation accuracy using inertial navigation systems in autonomous underwater vehicles. OCEANS, 2011 IEEE-Spain (pp. 1–7).Google Scholar
PHINS. (2013). Photonic Inertial Sensors, IXBLUE, France. https://www.ixblue.com/products/phins. Accessed 30 March 2013.Google Scholar
PNI sensors corporation. (2014) PNI sensors corporation. CA, USA. http://www.precisionnav.com/products/tcm. Accessed 25 January 2014 Google Scholar
Ramadass, G. A., Vedachalam, N., Balanagajyothi, V., Ramesh, R. and Atmanand, M. A. (2013). A modeling tool for sensor selection for inertial navigation systems used in underwater vehicles. Ocean Electronics (SYMPOL), 2013 (pp. 175–188). IEEE. Kochi, India.CrossRefGoogle Scholar
Simon, D. (2006). Optimal state estimation: Kalman, H infinity, and nonlinear approaches. John Wiley & Sons.CrossRefGoogle Scholar
Teledyne RD Instruments. (2013). http://www.rdinstruments.com/pdfs/wh_navigator_ds_lr.pdf. Accessed 20 January 2013 Google Scholar
Titterton, D.H. and Weston, J.L. (2004). Strapdown inertial navigation technology. The Institution of Electrical Engineers (pp. 344–345).Google Scholar
Vedachalam, N., Ramadass, G. A. and Atmanand, M. A. (2014a). Reliability centered modeling for development of deep water Human Occupied Vehicles. Applied Ocean Research, 46, 131–143.CrossRefGoogle Scholar
Vedachalam, N., Ramadass, G. A. and Atmanand, M. A. (2014b). Review of Technological Advancements and HSE-Based Safety Model for Deep-Water Human Occupied Vehicles. Marine Technology Society Journal, 48(3), 25–42.CrossRefGoogle Scholar
Vedachalam, N., Ramesh, S., Subramanian, A. N., Sathianarayanan, D., Ramesh, R., Harikrishnan, G. and Atmanand, M. A. (2015). Design and development of Remotely Operated Vehicle for shallow waters and polar research. In Underwater Technology (UT), IEEE (pp. 1–5). Chennai, India.Google Scholar
Welch, G. and Bishop, G. (1995). An introduction to the Kalman filter. Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC.Google Scholar